/usr/share/octave/packages/statistics-1.3.0/nanstd.m is in octave-statistics 1.3.0-1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 | ## Copyright (C) 2001 Paul Kienzle <pkienzle@users.sf.net>
##
## This program is free software; you can redistribute it and/or modify it under
## the terms of the GNU General Public License as published by the Free Software
## Foundation; either version 3 of the License, or (at your option) any later
## version.
##
## This program is distributed in the hope that it will be useful, but WITHOUT
## ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
## FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
## details.
##
## You should have received a copy of the GNU General Public License along with
## this program; if not, see <http://www.gnu.org/licenses/>.
## -*- texinfo -*-
## @deftypefn {Function File} {@var{v} =} nanstd (@var{X})
## @deftypefnx{Function File} {@var{v} =} nanstd (@var{X}, @var{opt})
## @deftypefnx{Function File} {@var{v} =} nanstd (@var{X}, @var{opt}, @var{dim})
## Compute the standard deviation while ignoring NaN values.
##
## @code{nanstd} is identical to the @code{std} function except that NaN values are
## ignored. If all values are NaN, the standard deviation is returned as NaN.
## If there is only a single non-NaN value, the deviation is returned as 0.
##
## The argument @var{opt} determines the type of normalization to use. Valid values
## are
##
## @table @asis
## @item 0:
## normalizes with @math{N-1}, provides the square root of best unbiased estimator of
## the variance [default]
## @item 1:
## normalizes with @math{N}, this provides the square root of the second moment around
## the mean
## @end table
##
## The third argument @var{dim} determines the dimension along which the standard
## deviation is calculated.
##
## @seealso{std, nanmin, nanmax, nansum, nanmedian, nanmean}
## @end deftypefn
function v = nanstd (X, opt, varargin)
if nargin < 1
print_usage;
else
if nargin < 3
dim = min(find(size(X)>1));
if isempty(dim), dim=1; endif;
else
dim = varargin{1};
endif
if ((nargin < 2) || isempty(opt))
opt = 0;
endif
## determine the number of non-missing points in each data set
n = sum (!isnan(X), varargin{:});
## replace missing data with zero and compute the mean
X(isnan(X)) = 0;
meanX = sum (X, varargin{:}) ./ n;
## subtract the mean from the data and compute the sum squared
sz = ones(1,length(size(X)));
sz(dim) = size(X,dim);
v = sumsq (X - repmat(meanX,sz), varargin{:});
## because the missing data was set to zero each missing data
## point will contribute (-meanX)^2 to sumsq, so remove these
v = v - (meanX .^ 2) .* (size(X,dim) - n);
if (opt == 0)
## compute the standard deviation from the corrected sumsq using
## max(n-1,1) in the denominator so that the std for a single point is 0
v = sqrt ( v ./ max(n - 1, 1) );
elseif (opt == 1)
## compute the standard deviation from the corrected sumsq
v = sqrt ( v ./ n );
else
error ("std: unrecognized normalization type");
endif
## make sure that we return a real number
v = real (v);
endif
endfunction
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